DeepMark: One-Shot Clothing Detection

TL;DR

DeepMark introduces a one-shot clothing detection method based on CenterNet, achieving high accuracy on DeepFashion2 dataset and suitable for low-power devices.

Contribution

It presents a novel one-shot approach for clothing detection that improves speed and accuracy over existing methods.

Findings

Achieved 0.723 mAP for bounding box detection

Achieved 0.532 mAP for landmark detection

Effective on low-power devices

Abstract

The one-shot approach, DeepMark, for fast clothing detection as a modification of a multi-target network, CenterNet, is proposed in the paper. The state-of-the-art accuracy of 0.723 mAP for bounding box detection task and 0.532 mAP for landmark detection task on the DeepFashion2 Challenge dataset were achieved. The proposed architecture can be used effectively on the low-power devices.